This is the free Amazon MLA-C01 practice question bank —
120 of 235 total questions, each with a full explanation, free to
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Question 1
An ML engineer uses A/B testing to dynamically select recommendation models. The models are deployed on Amazon SageMaker AI endpoints. The ML engineer needs to monitor system metrics such as latency, call volume, and HTTP status codes when the endpoints are invoked.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS X-Ray tracing to monitor SageMaker AI endpoints.
B. Configure Amazon CloudWatch dashboards with AWS Lambda log processors.
C. Enable AWS Identity and Access Management (IAM) Access Analyzer to track SageMaker AI endpoint metrics.
D. Deploy AWS Trusted Advisor checks on SageMaker AI endpoint instances.
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker endpoints publish invocation metrics such as latency, invocation count, and error/status metrics natively to Amazon CloudWatch. Using CloudWatch is the managed, low-operational-overhead monitoring solution. While the wording mentions Lambda log processors, the intent is CloudWatch-based monitoring; the other options (X-Ray, IAM Access Analyzer, Trusted Advisor) do not provide native endpoint metric monitoring for these system metrics.
Question 2
A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographics data of the customers.
Which solution will meet these requirements?
A. Use SageMaker Clarify to generate a bias report. Send the report to the auditor.
B. Use AWS Glue DataBrew to create a job to detect drift in the model's data quality. Send the job output to the auditor.
C. Use Amazon Quick Suite (previously known as Amazon QuickSight) integration with SageMaker AI to generate a bias report from Quick Suite. Send the report to the auditor.
D. Use Amazon CloudWatch metrics from the SageMaker AI namespace to create a bias dashboard. Share the dashboard with the auditor.
Show Answer
Correct Answer: A
Explanation: Amazon SageMaker Clarify is designed to detect and explain bias in machine learning datasets and models, including analysis across sensitive demographic attributes. It generates bias reports suitable for auditing. AWS Glue DataBrew focuses on data preparation and quality, not ML bias; Quick Suite does not generate SageMaker bias reports; CloudWatch metrics monitor operational aspects rather than providing bias explanations.
Question 3
An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker Al notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.
The lead ML engineer must see the latest SageMaker AI notebook updates.
Which solution will meet this requirement?
A. Run the !git pull origin master command.
B. Run the !git commit command.
C. Run the !git push origin master command.
D. Run the !git branch command.
Show Answer
Correct Answer: A
Explanation: To update the local notebook repository with the latest merged changes from the remote Git repository, the engineer should run `git pull origin master`. This fetches the latest commits from the remote `master` branch and merges them into the local branch. `git commit` only records local changes, `git push` uploads local commits to the remote, and `git branch` only lists or manages branches.
Question 4
A company wants to use large language models (LLMs) that are supported by Amazon Bedrock to develop a chat interface for the company’s internal technical documentation. The company stores the documentation as dozens of text files that are several megabytes in total size. The company updates the text files often.
Which solution will meet these requirements MOST cost-effectively?
A. Create a new LLM on Amazon Bedrock. Train the new LLM on the original dataset and the company documentation. Make the new model available in Bedrock for calls from the chat interface.
B. Integrate the company documentation with Amazon Bedrock guardrails. Invoke the guardrails for all Amazon Bedrock calls from the chat interface.
C. Use all the text files to fine tune a model in Amazon Bedrock. Use the fine-tuned model to process user prompts.
D. Upload all the text files to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when the chat interface makes calls to Amazon Bedrock.
Show Answer
Correct Answer: D
Explanation: Amazon Bedrock Knowledge Bases implement a retrieval-augmented generation (RAG) pattern by indexing the company's documents and retrieving relevant content at query time. This is ideal for documentation that changes frequently because updates only require refreshing the knowledge base rather than retraining or fine-tuning a model. Creating or training a new model is unnecessary and expensive, fine-tuning is not appropriate for frequently changing reference documents, and guardrails are for safety and policy enforcement, not knowledge injection.
Question 5
A company is performing A/B testing on a model that provides product recommendations. The company has deployed two versions of the model and is showing each version of the model to 50% of users randomly.
Which metric should the company use to evaluate whether users act on a recommendation?
A. The conversion rates between the two versions of the model
B. The number of recommendations provided to each user
C. The model accuracy on held-out test data
D. The latency of model inference
Show Answer
Correct Answer: A
Explanation: In an A/B test for recommendation systems, the key outcome is whether users take the desired action after receiving recommendations. Conversion rate directly measures user response (such as clicking, purchasing, or otherwise acting on recommendations) and is the appropriate metric for comparing the two model versions. Recommendation count, offline accuracy, and inference latency do not directly measure user adoption of recommendations.
Question 6
A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model’s performance has degraded over time.
Which solution can the company use to identify model drift in the future?
A. Create a monitoring job in SageMaker Model Monitor. Then create a baseline from the training dataset.
B. Create a baseline from the training dataset. Then create a monitoring job in SageMaker Model Monitor.
C. Create a baseline by using a built-in rule in SageMaker Clarify. Monitor the drift in Amazon CloudWatch.
D. Retrain the model on new data. Compare the retrained model's performance to the original model’s performance.
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker Model Monitor detects data and model quality drift by first creating a baseline from the training dataset (capturing expected statistics and constraints), then running a monitoring job that compares production inference data against that baseline. This is the standard workflow for ongoing drift detection.
Question 7
An ML engineer used Amazon SageMaker Studio to train a neural network. The neural network logs its information into TensorBoard and uses stochastic gradient descent (SGD) as the optimizer.
The ML engineer reviewed training graphs and discovered that the accuracy was not increasing and the loss was decreasing very slowly. The ML engineer needs to improve the model’s performance without increasing the total training time.
Which solution will meet these requirements?
A. Increase the initial learning rate.
B. Decrease the initial learning rate.
C. Increase the total number of epochs.
D. Decrease the total number of epochs.
Show Answer
Correct Answer: A
Explanation: If loss is decreasing very slowly and accuracy is not improving, the SGD optimizer is likely making updates that are too small due to a learning rate that is too low. Increasing the initial learning rate can accelerate convergence without increasing total training time. Decreasing the learning rate would slow training further, increasing epochs would increase training time, and decreasing epochs would likely worsen performance.
Question 8
An ML engineer is importing a custom model from the Hugging Face Hub into Amazon Bedrock. The ML engineer wants to use the model with the Amazon Bedrock API in an agentic AI application.
Which combination of steps will meet these requirements? (Choose two.)
A. Import the model for deployment by using Amazon Bedrock On-Demand mode.
B. Convert the model to a quantized format. Import the quantized model after conversion.
C. Import the model directly by using the Amazon SageMaker AI SDK in the agentic AI application.
D. During the model import process, configure an auto-scaling policy before using the model in the agentic AI application.
E. In the import job, provide the complete Amazon S3 URI that points to the Hugging Face model files. Ensure that the S3 URI is in the same AWS account as the Amazon Bedrock import job.
Show Answer
Correct Answer: A, E
Explanation: Amazon Bedrock custom model import supports importing model artifacts from Amazon S3 and then deploying the imported model for inference through the Bedrock API using On-Demand inference. The import job requires the full S3 URI for the model artifacts in the same AWS account. Quantization is not a required import step, SageMaker SDK is not used to import models into Bedrock for Bedrock API use, and auto-scaling is not configured during the import job.
Question 9
A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Rekognition and AWS Lambda to extract and analyze the metadata from the videos’ image frames.
B. Use Amazon Rekognition and a large language model (LLM) hosted on Amazon Bedrock to extract and analyze the metadata from the videos’ image frames.
C. Use Amazon SageMaker AI to extract and analyze the metadata from the videos’ image frames.
D. Use Amazon Transcribe and Amazon Comprehend to extract and analyze the metadata from the videos’ image frames.
Show Answer
Correct Answer: A
Explanation: Amazon Rekognition provides managed image and video analysis, including content moderation capabilities suitable for near real-time processing. AWS Lambda can orchestrate serverless extraction and analysis of video frames with minimal operational overhead. SageMaker requires managing ML deployments, Bedrock LLMs are not the appropriate primary service for visual moderation, and Transcribe plus Comprehend analyze speech/text rather than image frames.
Question 10
A company wants to launch a new website feature that predicts home prices based on user-supplied home attributes. The attributes include location, square footage, and number of bedrooms and bathrooms.
An ML engineer has trained a regression model by using the Amazon SageMaker AI XGBoost algorithm. The model performs well with training data. However, the model significantly underperforms when it is validated against real-world data.
Which solution will improve the model’s validation score with the LEAST implementation effort?
A. Create a larger training dataset that includes more real-world data. Retrain the model.
B. Increase the value of the num_round hyperparameter.
C. Change the eval_metric hyperparameter from Root Mean Square Error (RMSE) to Error.
D. Increase the value of the lambda hyperparameter.
Show Answer
Correct Answer: A
Explanation: The model performs well on the training data but poorly on real-world validation data, which indicates overfitting or a mismatch between the training distribution and production data. The most effective low-effort improvement is to retrain with a larger, more representative dataset that includes real-world examples. Increasing num_round is more likely to worsen overfitting, changing eval_metric only changes evaluation and not model quality, and increasing lambda can add regularization but is less likely to address a data distribution problem than improving the training data.
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